The present invention relates to bone segmentation and landmark detection for joint replacement surgery, and more particularly, to bone segmentation and landmark detection in medical image data during a planning stage of joint replacement surgery.
According to the American Academy of Orthopedic Surgeons, total knee replacement surgery is performed for more than 500,000 patients each year in the United States alone. This number has been rising in recent decades partly due to the aging population, but also due to improved outcomes of the procedure, streamlined workflow, and longevity of knee implants, which have resulted in more patients being eligible for the procedure. The goal of total knee replacement surgery is to replace a damaged knee joint and as a result, alleviate pain, improve mobility, and improve the patient's quality of life.
In recent years, the total knee replacement surgery workflow has improved by introducing personalized instruments and patient-specific knee implants. Patient-specific surgery reduces the length of the procedure by eliminating several steps (e.g., measurement steps) and minimizes the amount of residual bone during the resection. Patient-specific total knee replacement procedure is typically performed in two stages: a planning stage and an intervention stage. In the planning stage, a magnetic resonance imaging (MRI) or computed tomography (CT) scan of the patient's leg is acquired, and the physician uses the scan to build a full 3D model of the patient's leg. The model is combined with patient information and surgical preferences to create a patient proposal. The proposal is used to manufacture cutting guides and plastic blocks specific for that patient. These instruments are then used to accurately cut the damaged bone and position the implant during the intervention stage.